Surgery simulation is a technology based on interactive biomechanics as well as haptic and visual rendering for training novice surgeons. Another important component of a surgery simulator is the representation of a patient's anatomy in terms of simple shapes, typically triangles and tetrahedra, coinciding with relevant tissues, whose manipulation, cutting and resection is simulated. While this technology has so far emphasized generic simulators, increasingly researchers are emphasizing models that derive from the images of patients whose pathology collectively are predictive of a significant proportion of the future caseload of surgeons. In addition, our philosophy of producing patient-specific neurosurgery simulators is to organize the architecture in terms of two parameters: the choice of approach- pterional, trans-nasal, frontal and so on, and the nature of the pathology, which in turn determines the specific nature of the treatment and choice of tools. This paper describes on-going work on the refinement of an open-source software pipeline for producing patient-specific neurosurgery simulation, based on segmentation tools such as those available in Slicer and BrainVisa, surface and volume meshing, such as public VTK-based tools and Tetgen respectively, and the SOFA simulation platform. Practical requirements of the various components of the pipeline, such as resolution control and fidelity of surface and volume meshing, and interactive nonlinear mechanics in the biomechanics engine, are discussed.

This paper describes an a novel surgical ontology based approach with haptics for neurosurgical operations. The work describes a detailed segmentation of various sulcal and gyral areas for the cortical surface and some interesting mesh generation approaches in defining various folds

and surface characteristics.

The work then helps to intrepret definition of a mesh based surface through precise monitoring and control for definition of underlying surgical ontology thereby giving the clinician control over neurosurgical simulation and operating procedure. The kind of impact and processing is at high granularity in terms of mesh definition and refinement.

The authors present building blocks in creating a neurosurgical pipeline and framework by which you can alter the decision making process with sufficient geometric and information centric knowledge

of the system developed.

Hypothesis:

The authors make few assumptions regarding coarseness of meshes that can be generated, principally, one assumes that such a mesh will exist and sufficient resolution and can be recreated from MR scans.

One fundamental assumption that shines through is that there is indeed a need for a flexible approach towards information centric decision making for neurosurgical procedures and this I believe is from existing reference literature and retrospectice studies.

Evidence:

The evidence is from finite element simulations of the surgical process using the SOFA framework which helps present a mesh based deformation approach to surgical simulation.

Snapshots in 3.4 illustrate some components of such as a decision support system and neurosurgical pipeline based approach to perform surgery and is in the right direction towards providing

evidence for the use of this kind of idea for surgical simulation. More use cases or clinical examples would be valuable.

The authors definitely have evidence for a pedantic system for neurosurgical mesh processing and surgical preordering with the system proposed.

Open Science:

I dont there is supporting code as part of this paper submission. The code and data might be part of an earlier software build that I'd have to find and compile to provide comments for this section.

Enough details are not provided currently but might available as part of this submission through an earlier release.

Reproducibility:

No I havent downloaded or compiled the code. I'd like to to be able to review mesh refinement quality and neuro pipeline generation since this seems like an important information centric framework to adopt and use.

Quality of the data :

There is no data on the paper review system that might be part of an earlier build so I cant comment on these topics.

Interest:

This work has immense benefit in the MR elastography world for computational simulation.